Burstification Queue Management in Optical Burst Switching Networks

Burstification Queue Management in Optical Burst Switching Networks Among the various issues lying in optical burst switching (OBS) networks, burstification, i.e., assembling multiple IP packets into bursts, is an important one. Between the two important aspects related to burst assembly, the burst assembly algorithm aspect has been extensively studied in the literature. However, as far as we know, there is no research about the burstification queue management (BQM) aspect, which refers to how many burstification queues (BQ) we should set at each OBS edge node and how to manage these BQs. Suppose there are G destinations (egress edge nodes) and the OBS network provides S different quality of service (QoS) classes. Traditionally, it is simply regarded that each ingress edge node needs G· S queues to sort incoming packets, one for each possible destination and QoS class. For simplicity, we call this policy the static dedicate BQM (SDB) policy. The SDB policy, though simple, lacks scalability since we have to add S BQs at each OBS edge node if an extra OBS edge node is added to the OBS network. To solve this problem, we propose in this paper two BQM policies: quasi-static BQM (QSB) policy and dynamic BQM (DB) policy. For the QSB policy, we derive the packet loss probability due to lacking BQs based on a Markov chain, from which we can work out the employed number of BQs for a given packet loss probability. Based on these results, the scalability of the QSB policy is also studied. With the DB policy, we not only can dynamically assign BQs for incoming packets, but also can dynamically allocate buffer capacity for each BQ by using a least-mean-square (LMS)-based linear prediction filter. The performance of the DB policy is investigated by analysis and extensive simulations. We also compared the performance of the QSB policy and the DB policy. Results from analysis and simulation demonstrate that the DB policy is the best. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Photonic Network Communications Springer Journals

Burstification Queue Management in Optical Burst Switching Networks

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Publisher
Springer Journals
Copyright
Copyright © 2006 by Springer Science + Business Media, Inc.
Subject
Computer Science; Computer Communication Networks; Electrical Engineering; Characterization and Evaluation of Materials
ISSN
1387-974X
eISSN
1572-8188
D.O.I.
10.1007/s11107-006-5326-y
Publisher site
See Article on Publisher Site

Abstract

Among the various issues lying in optical burst switching (OBS) networks, burstification, i.e., assembling multiple IP packets into bursts, is an important one. Between the two important aspects related to burst assembly, the burst assembly algorithm aspect has been extensively studied in the literature. However, as far as we know, there is no research about the burstification queue management (BQM) aspect, which refers to how many burstification queues (BQ) we should set at each OBS edge node and how to manage these BQs. Suppose there are G destinations (egress edge nodes) and the OBS network provides S different quality of service (QoS) classes. Traditionally, it is simply regarded that each ingress edge node needs G· S queues to sort incoming packets, one for each possible destination and QoS class. For simplicity, we call this policy the static dedicate BQM (SDB) policy. The SDB policy, though simple, lacks scalability since we have to add S BQs at each OBS edge node if an extra OBS edge node is added to the OBS network. To solve this problem, we propose in this paper two BQM policies: quasi-static BQM (QSB) policy and dynamic BQM (DB) policy. For the QSB policy, we derive the packet loss probability due to lacking BQs based on a Markov chain, from which we can work out the employed number of BQs for a given packet loss probability. Based on these results, the scalability of the QSB policy is also studied. With the DB policy, we not only can dynamically assign BQs for incoming packets, but also can dynamically allocate buffer capacity for each BQ by using a least-mean-square (LMS)-based linear prediction filter. The performance of the DB policy is investigated by analysis and extensive simulations. We also compared the performance of the QSB policy and the DB policy. Results from analysis and simulation demonstrate that the DB policy is the best.

Journal

Photonic Network CommunicationsSpringer Journals

Published: Jan 1, 2006

References

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